Optimal Transport for Unsupervised Denoising Learning
| Autor: | Wei Wang, Fei Wen, Zeyu Yan, Peilin Liu |
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| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Applied Mathematics Image and Video Processing (eess.IV) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing Machine Learning (cs.LG) Computational Theory and Mathematics Artificial Intelligence FOS: Electrical engineering electronic engineering information engineering Computer Vision and Pattern Recognition Software |
| Zdroj: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 45:2104-2118 |
| ISSN: | 1939-3539 0162-8828 |
| DOI: | 10.1109/tpami.2022.3170155 |
| Popis: | Recently, much progress has been made in unsupervised denoising learning. However, existing methods more or less rely on some assumptions on the signal and/or degradation model, which limits their practical performance. How to construct an optimal criterion for unsupervised denoising learning without any prior knowledge on the degradation model is still an open question. Toward answering this question, this work proposes a criterion for unsupervised denoising learning based on the optimal transport theory. This criterion has favorable properties, e.g., approximately maximal preservation of the information of the signal, whilst achieving perceptual reconstruction. Furthermore, though a relaxed unconstrained formulation is used in practical implementation, we prove that the relaxed formulation in theory has the same solution as the original constrained formulation. Experiments on synthetic and real-world data, including realistic photographic, microscopy, depth, and raw depth images, demonstrate that the proposed method even compares favorably with supervised methods, e.g., approaching the PSNR of supervised methods while having better perceptual quality. Particularly, for spatially correlated noise and realistic microscopy images, the proposed method not only achieves better perceptual quality but also has higher PSNR than supervised methods. Besides, it shows remarkable superiority in harsh practical conditions with complex noise, e.g., raw depth images. Code is available at https://github.com/wangweiSJTU/OTUR. Published in IEEE TPAMI, DOI: 10.1109/TPAMI.2022.3170155, https://ieeexplore.ieee.org/document/9763342 (40 pages, 33 figures) |
| Databáze: | OpenAIRE |
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